Crop disease recognition and yield estimation

ABSTRACT

In an embodiment, a computer-implemented method is disclosed. The method comprises causing a camera to continuously capture surroundings to generate multiple images and causing a display device to continuously display the multiple images as the multiple images are generated. In addition, the method comprises processing each of one or more of the multiple images. The processing comprises identifying at least one of a plurality of diseases and calculating at least one disease score associated with the at least one disease for a particular image; causing the display device to display information regarding the at least one disease and the at least one disease score in association with a currently displayed image; receiving input specifying one or more of the at least one disease; and causing the display device to show additional data regarding the one or more diseases, including a remedial measure for the one or more diseases.

BENEFIT CLAIM

This application claims the benefit under 35 U.S.C. § 120 as acontinuation of U.S. patent application Ser. No. 15/688,567, filed Aug.28, 2017, the entire contents of which are hereby incorporated byreference for all purposes as if fully set forth herein.

COPYRIGHT NOTICE

A portion of the disclosure of this patent document contains materialwhich is subject to copyright protection. The copyright owner has noobjection to the facsimile reproduction by anyone of the patent documentor the patent disclosure, as it appears in the Patent and TrademarkOffice patent file or records, but otherwise reserves all copyright orrights whatsoever. © 2019 The Climate Corporation.

FIELD OF THE DISCLOSURE

The present disclosure relates to crop field evaluation and morespecifically to detection of crop diseases and estimation of crop yield.

BACKGROUND

The approaches described in this section are approaches that could bepursued, but not necessarily approaches that have been previouslyconceived or pursued. Therefore, unless otherwise indicated, it shouldnot be assumed that any of the approaches described in this sectionqualify as prior art merely by virtue of their inclusion in thissection.

A grower can manage a number of crop fields. Given the possible sizes ofthe fields and the numbers of plants in these fields, it can betime-consuming and labor-intensive to evaluate the status, maintain thehealth, and maximize the yield of the fields. It can be helpful to havetools that assist a grower in discovering the occurrence of cropdiseases or in estimating the current crop yield.

SUMMARY

The appended claims may serve as a summary of the disclosure.

BRIEF DESCRIPTION OF THE DRAWINGS

In the drawings:

FIG. 1 illustrates an example computer system that is configured toperform the functions described herein, shown in a field environmentwith other apparatus with which the system may interoperate.

FIG. 2 illustrates two views of an example logical organization of setsof instructions in main memory when an example mobile application isloaded for execution.

FIG. 3 illustrates a programmed process by which the agriculturalintelligence computer system generates one or more preconfiguredagronomic models using agronomic data provided by one or more datasources.

FIG. 4 is a block diagram that illustrates a computer system upon whichan embodiment of the invention may be implemented.

FIG. 5 depicts an example embodiment of a timeline view for data entry.

FIG. 6 depicts an example embodiment of a spreadsheet view for dataentry.

FIG. 7 illustrates example components of a client computer.

FIG. 8 illustrates an example process performed by a client computer tomanage a field evaluation process.

FIG. 9 illustrates an example computer interaction device showing agraphical user interface (“GUI”) under program control that allows auser to invoke the disease recognition functionality.

FIG. 10 illustrates an example computer interaction device showing a GUIunder program control that can provide a summary of or tutorial on howto realize the disease recognition functionality.

FIG. 11 illustrates an example computer interaction device showing a GUIunder program control that can display results of analyzing generatedimages and receive requests to analyze select generated images.

FIG. 12 illustrates an example computer interaction device showing a GUIunder program control that can provide additional information aboutrecognized diseases.

FIG. 13 illustrates an example computer interaction device showing a GUIunder program control that can enable a user to store or upload agenerated image or provide metadata for the generated image.

FIG. 14 illustrates an example process typically performed by a clientcomputer to calculate the yield of a crop, such as the number of kernelsin an ear of corn (maize).

FIG. 15 illustrates an example image that has been enhanced in contrastand segmented into a foreground containing a corn ear and a background.

FIG. 16 illustrates an example computer interaction device showing a GUIunder program control that can provide a summary of or tutorial on howto realize the yield estimation functionality.

FIG. 17 illustrates an example computer interaction device showing a GUIunder program control that can display yield information.

DETAILED DESCRIPTION

In the following description, for the purposes of explanation, numerousspecific details are set forth in order to provide a thoroughunderstanding of the present disclosure. It will be apparent, however,that embodiments may be practiced without these specific details. Inother instances, well-known structures and devices are shown in blockdiagram form in order to avoid unnecessarily obscuring the presentdisclosure. Embodiments are disclosed in sections according to thefollowing outline:

1. GENERAL OVERVIEW

2. EXAMPLE AGRICULTURAL INTELLIGENCE COMPUTER SYSTEM

-   -   2.1. STRUCTURAL OVERVIEW    -   2.2. APPLICATION PROGRAM OVERVIEW    -   2.3. DATA INGEST TO THE COMPUTER SYSTEM    -   2.4 PROCESS OVERVIEW—AGRONOMIC MODEL TRAINING    -   2.5 DISEASE RECOGNITION AND YIELD ESTIMATION    -   2.6 IMPLEMENTATION EXAMPLE—HARDWARE OVERVIEW

3. FUNCTIONAL DESCRIPTION

-   -   3.1 CONSTRUCTION OF A COMPUTER PROGRAM FOR MANAGING A FIELD        EVALUATION PROCESS        -   3.1.1 RECOGNIZING DISEASES OR CROP TYPES        -   3.1.2 ESTIMATING CROP YIELD    -   3.2 EXECUTION OF THE COMPUTER PROGRAM FOR MANAGING A FIELD        EVALUATION PROCESS        -   3.2.1 RECOGNIZING DISEASES OR CROP TYPES        -   3.2.2 ESTIMATING CROP YIELD

1. General Overview

A disease recognition and yield estimation system and related methodsare disclosed. in some embodiments, the system includes a server and oneor more client computers. The server can be programmed or configuredwith data structures and/or database records that are arranged todevelop models or techniques for identifying diseases or crop types andestimating crop yield. The server can also be configured to build acomputer program that invokes the models or techniques and deploy thecomputer program to each of the client computers. Each client computercan be a mobile device, such as a cellular phone or a tablet, which canbe used by a grower as the grower walks through one of the crop fields.By executing the computer program, such as a mobile app, the clientcomputer can be programmed or configured with data structures and/ordatabase records that are arranged to continuously scan the crops in thefield and recognize the type of a crop or detect an occurrence of adisease in a crop in real time. In response to detecting an occurrenceof a disease, the client computer can be programmed to cause displayinformation regarding the occurrence and additional data about thedisease, such as possible causes and treatment regimes. In response torecognizing corn, for example, the client computer can be programmed tocause display of a kernel count and additional information, such as anestimated corn yield for the field. In this disclosure, the term “corn”is equivalent to “maize”.

The disease recognition and yield estimation system and related methodsproduce many technical benefits. First, the system and methods applyrobust classification and image processing techniques and produceaccurate diagnosis of crop health and estimation of crop yield. Second,the system and methods manage an end-to-end process from entering a cropfield to receiving a field-wide evaluation of crop growth by properlycoordinating communication among different electronic components, suchas a processor, an image capturing device, and a display device. Third,the system and methods generally require a relatively small footprintthat allows efficient utilization of resources typically found in amobile device and thus can readily be implemented by such a device,leading to reduced digital communication overhead and manual handlingissues. Third, the system and methods are designed to handle continuousdata streams and achieve high processing throughput, thereby offeringcomprehensive information in near real time.

2. Example Agricultural Intelligence Computer System

2.1 Structural Overview

FIG. 1 illustrates an example computer system that is configured toperform the functions described herein, shown in a field environmentwith other apparatus with which the system may interoperate. In oneembodiment, a user 102 owns, operates or possesses a field managercomputing device 104 in a field location or associated with a fieldlocation such as a field intended for agricultural activities or amanagement location for one or more agricultural fields. The fieldmanager computer device 104 is programmed or configured to provide fielddata 106 to an agricultural intelligence computer system 130 via one ormore networks 109.

Examples of field data 106 include (a) identification data (for example,acreage, field name, field identifiers, geographic identifiers, boundaryidentifiers, crop identifiers, and any other suitable data that may beused to identify farm land, such as a common land unit (CLU), lot andblock number, a parcel number, geographic coordinates and boundaries,Farm Serial Number (FSN), farm number, tract number, field number,section, township, and/or range), (b) harvest data (for example, croptype, crop variety, crop rotation, whether the crop is grownorganically, harvest date, Actual Production History (APH), expectedyield, yield, crop price, crop revenue, grain moisture, tillagepractice, and previous growing season information), (c) soil data (forexample, type, composition, pH, organic matter (OM), cation exchangecapacity (CEC)), (d) planting data (for example, planting date, seed(s)type, relative maturity (RM) of planted seed(s), seed population), (e)fertilizer data (for example, nutrient type (Nitrogen, Phosphorous,Potassium), application type, application date, amount, source, method),(f) pesticide data (for example, pesticide, herbicide, fungicide, othersubstance or mixture of substances intended for use as a plantregulator, defoliant, or desiccant, application date, amount, source,method), (g) irrigation data (for example, application date, amount,source, method), (h) weather data (for example, precipitation, rainfallrate, predicted rainfall, water runoff rate region, temperature, wind,forecast, pressure, visibility, clouds, heat index, dew point, humidity,snow depth, air quality, sunrise, sunset), (i) imagery data (forexample, imagery and light spectrum information from an agriculturalapparatus sensor, camera, computer, smartphone, tablet, unmanned aerialvehicle, planes or satellite), (j) scouting observations (photos,videos, free form notes, voice recordings, voice transcriptions, weatherconditions (temperature, precipitation (current and over time), soilmoisture, crop growth stage, wind velocity, relative humidity, dewpoint, black layer)), and (k) soil, seed, crop phenology, pest anddisease reporting, and predictions sources and databases.

A data server computer 108 is communicatively coupled to agriculturalintelligence computer system 130 and is programmed or configured to sendexternal data 110 to agricultural intelligence computer system 130 viathe network(s) 109. The external data server computer 108 may be ownedor operated by the same legal person or entity as the agriculturalintelligence computer system 130, or by a different person or entitysuch as a government agency, non-governmental organization (NGO), and/ora private data service provider. Examples of external data includeweather data, imagery data, soil data, or statistical data relating tocrop yields, among others. External data 110 may consist of the sametype of information as field data 106. In some embodiments, the externaldata 110 is provided by an external data server 108 owned by the sameentity that owns and/or operates the agricultural intelligence computersystem 130. For example, the agricultural intelligence computer system130 may include a data server focused exclusively on a type of data thatmight otherwise be obtained from third party sources, such as weatherdata. In some embodiments, an external data server 108 may actually beincorporated within the system 130.

An agricultural apparatus 111 may have one or more remote sensors 112fixed thereon, which sensors are communicatively coupled either directlyor indirectly via agricultural apparatus 111 to the agriculturalintelligence computer system 130 and are programmed or configured tosend sensor data to agricultural intelligence computer system 130.Examples of agricultural apparatus 111 include tractors, combines,harvesters, planters, trucks, fertilizer equipment, unmanned aerialvehicles, and any other item of physical machinery or hardware,typically mobile machinery, and which may be used in tasks associatedwith agriculture. In some embodiments, a single unit of apparatus 111may comprise a plurality of sensors 112 that are coupled locally in anetwork on the apparatus; controller area network (CAN) is example ofsuch a network that can be installed in combines or harvesters.Application controller 114 is communicatively coupled to agriculturalintelligence computer system 130 via the network(s) 109 and isprogrammed or configured to receive one or more scripts to control anoperating parameter of an agricultural vehicle or implement from theagricultural intelligence computer system 130. For instance, acontroller area network (CAN) bus interface may be used to enablecommunications from the agricultural intelligence computer system 130 tothe agricultural apparatus 111, such as how the CLIMATE FIELDVIEW DRIVE,available from The Climate Corporation, San Francisco, Calif., is used.Sensor data may consist of the same type of information as field data106. In some embodiments, remote sensors 112 may not be fixed to anagricultural apparatus 111 but may be remotely located in the field andmay communicate with network 109.

The apparatus 111 may comprise a cab computer 115 that is programmedwith a cab application, which may comprise a version or variant of themobile application for device 104 that is further described in othersections herein. In an embodiment, cab computer 115 comprises a compactcomputer, often a tablet-sized computer or smartphone, with a graphicalscreen display, such as a color display, that is mounted within anoperator's cab of the apparatus 111. Cab computer 115 may implement someor all of the operations and functions that are described further hereinfor the mobile computer device 104.

The network(s) 109 broadly represent any combination of one or more datacommunication networks including local area networks, wide areanetworks, internetworks or internets, using any of wireline or wirelesslinks, including terrestrial or satellite links. The network(s) may beimplemented by any medium or mechanism that provides for the exchange ofdata between the various elements of FIG. 1. The various elements ofFIG. 1 may also have direct (wired or wireless) communications links.The sensors 112, controller 114, external data server computer 108, andother elements of the system each comprise an interface compatible withthe network(s) 109 and are programmed or configured to use standardizedprotocols for communication across the networks such as TCP/IP,Bluetooth, CAN protocol and higher-layer protocols such as HTTP, TLS,and the like.

Agricultural intelligence computer system 130 is programmed orconfigured to receive field data 106 from field manager computing device104, external data 110 from external data server computer 108, andsensor data from remote sensor 112. Agricultural intelligence computersystem 130 may be further configured to host, use or execute one or morecomputer programs, other software elements, digitally programmed logicsuch as FPGAs or ASICs, or any combination thereof to performtranslation and storage of data values, construction of digital modelsof one or more crops on one or more fields, generation ofrecommendations and notifications, and generation and sending of scriptsto application controller 114, in the manner described further in othersections of this disclosure.

In an embodiment, agricultural intelligence computer system 130 isprogrammed with or comprises a communication layer 132, presentationlayer 134, data management layer 140, hardware/virtualization layer 150,and model and field data repository 160. “Layer,” in this context,refers to any combination of electronic digital interface circuits,microcontrollers, firmware such as drivers, and/or computer programs orother software elements.

Communication layer 132 may be programmed or configured to performinput/output interfacing functions including sending requests to fieldmanager computing device 104, external data server computer 108, andremote sensor 112 for field data, external data, and sensor datarespectively. Communication layer 132 may be programmed or configured tosend the received data to model and field data repository 160 to bestored as field data 106.

Presentation layer 134 may be programmed or configured to generate agraphical user interface (GUI) to be displayed on field managercomputing device 104, cab computer 115 or other computers that arecoupled to the system 130 through the network 109. The GUI may comprisecontrols for inputting data to be sent to agricultural intelligencecomputer system 130, generating requests for models and/orrecommendations, and/or displaying recommendations, notifications,models, and other field data.

Data management layer 140 may be programmed or configured to manage readoperations and write operations involving the repository 160 and otherfunctional elements of the system, including queries and result setscommunicated between the functional elements of the system and therepository. Examples of data management layer 140 include JDBC, SQLserver interface code, and/or HADOOP interface code, among others.Repository 160 may comprise a database. As used herein, the term“database” may refer to either a body of data, a relational databasemanagement system (RDBMS), or to both. As used herein, a database maycomprise any collection of data including hierarchical databases,relational databases, flat file databases, object-relational databases,object oriented databases, and any other structured collection ofrecords or data that is stored in a computer system. Examples of RDBMS'sinclude, but are not limited to including, ORACLE®, MYSQL, IBM® DB2,MICROSOFT® SQL SERVER, SYBASE®, and POSTGRESQL databases. However, anydatabase may be used that enables the systems and methods describedherein.

When field data 106 is not provided directly to the agriculturalintelligence computer system via one or more agricultural machines oragricultural machine devices that interacts with the agriculturalintelligence computer system, the user may be prompted via one or moreuser interfaces on the user device (served by the agriculturalintelligence computer system) to input such information. In an exampleembodiment, the user may specify identification data by accessing a mapon the user device (served by the agricultural intelligence computersystem) and selecting specific CLUs that have been graphically shown onthe map. In an alternative embodiment, the user 102 may specifyidentification data by accessing a map on the user device (served by theagricultural intelligence computer system 130) and drawing boundaries ofthe field over the map. Such CLU selection or map drawings representgeographic identifiers. In alternative embodiments, the user may specifyidentification data by accessing field identification data (provided asshape files or in a similar format) from the U. S. Department ofAgriculture Farm Service Agency or other source via the user device andproviding such field identification data to the agriculturalintelligence computer system.

In an example embodiment, the agricultural intelligence computer system130 is programmed to generate and cause displaying a graphical userinterface comprising a data manager for data input. After one or morefields have been identified using the methods described above, the datamanager may provide one or more graphical user interface widgets whichwhen selected can identify changes to the field, soil, crops, tillage,or nutrient practices. The data manager may include a timeline view, aspreadsheet view, and/or one or more editable programs.

FIG. 5 depicts an example embodiment of a timeline view for data entry.Using the display depicted in FIG. 5, a user computer can input aselection of a particular field and a particular date for the additionof event. Events depicted at the top of the timeline may includeNitrogen, Planting, Practices, and Soil. To add a nitrogen applicationevent, a user computer may provide input to select the nitrogen tab. Theuser computer may then select a location on the timeline for aparticular field in order to indicate an application of nitrogen on theselected field. In response to receiving a selection of a location onthe timeline for a particular field, the data manager may display a dataentry overlay, allowing the user computer to input data pertaining tonitrogen applications, planting procedures, soil application, tillageprocedures, irrigation practices, or other information relating to theparticular field. For example, if a user computer selects a portion ofthe timeline and indicates an application of nitrogen, then the dataentry overlay may include fields for inputting an amount of nitrogenapplied, a date of application, a type of fertilizer used, and any otherinformation related to the application of nitrogen.

In an embodiment, the data manager provides an interface for creatingone or more programs. “Program,” in this context, refers to a set ofdata pertaining to nitrogen applications, planting procedures, soilapplication, tillage procedures, irrigation practices, or otherinformation that may be related to one or more fields, and that can bestored in digital data storage for reuse as a set in other operations.After a program has been created, it may be conceptually applied to oneor more fields and references to the program may be stored in digitalstorage in association with data identifying the fields. Thus, insteadof manually entering identical data relating to the same nitrogenapplications for multiple different fields, a user computer may create aprogram that indicates a particular application of nitrogen and thenapply the program to multiple different fields. For example, in thetimeline view of FIG. 5, the top two timelines have the “Fall applied”program selected, which includes an application of 150 lbs N/ac in earlyApril. The data manager may provide an interface for editing a program.In an embodiment, when a particular program is edited, each field thathas selected the particular program is edited. For example, in FIG. 5,if the “Fall applied” program is edited to reduce the application ofnitrogen to 130 lbs N/ac, the top two fields may be updated with areduced application of nitrogen based on the edited program.

In an embodiment, in response to receiving edits to a field that has aprogram selected, the data manager removes the correspondence of thefield to the selected program. For example, if a nitrogen application isadded to the top field in FIG. 5, the interface may update to indicatethat the “Fall applied” program is no longer being applied to the topfield. While the nitrogen application in early April may remain, updatesto the “Fall applied” program would not alter the April application ofnitrogen.

FIG. 6 depicts an example embodiment of a spreadsheet view for dataentry. Using the display depicted in FIG. 6, a user can create and editinformation for one or more fields. The data manager may includespreadsheets for inputting information with respect to Nitrogen,Planting, Practices, and Soil as depicted in FIG. 6. To edit aparticular entry, a user computer may select the particular entry in thespreadsheet and update the values. For example, FIG. 6 depicts anin-progress update to a target yield value for the second field.Additionally, a user computer may select one or more fields in order toapply one or more programs. In response to receiving a selection of aprogram for a particular field, the data manager may automaticallycomplete the entries for the particular field based on the selectedprogram. As with the timeline view, the data manager may update theentries for each field associated with a particular program in responseto receiving an update to the program. Additionally, the data managermay remove the correspondence of the selected program to the field inresponse to receiving an edit to one of the entries for the field.

In an embodiment, model and field data is stored in model and field datarepository 160. Model data comprises data models created for one or morefields. For example, a crop model may include a digitally constructedmodel of the development of a crop on the one or more fields. “Model,”in this context, refers to an electronic digitally stored set ofexecutable instructions and data values, associated with one another,which are capable of receiving and responding to a programmatic or otherdigital call, invocation, or request for resolution based upon specifiedinput values, to yield one or more stored output values that can serveas the basis of computer-implemented recommendations, output datadisplays, or machine control, among other things. Persons of skill inthe field find it convenient to express models using mathematicalequations, but that form of expression does not confine the modelsdisclosed herein to abstract concepts; instead, each model herein has apractical application in a computer in the form of stored executableinstructions and data that implement the model using the computer. Themodel may include a model of past events on the one or more fields, amodel of the current status of the one or more fields, and/or a model ofpredicted events on the one or more fields. Model and field data may bestored in data structures in memory, rows in a database table, in flatfiles or spreadsheets, or other forms of stored digital data.

The disease recognition and yield estimation server 170 (“the server”)comprises a set of one or more pages of main memory, such as RAM, in theagricultural intelligence computer system 130 into which executableinstructions have been loaded and which when executed cause theagricultural intelligence computing system to perform the functions oroperations that are described herein with reference to those modules.For example, the model management component 176 may comprise a set ofpages in RAM that contain instructions which when executed causeperforming the nutrient modeling functions that are described herein.The instructions may be in machine executable code in the instructionset of a CPU and may have been compiled based upon source code writtenin JAVA, C, C++, OBJECTIVE-C, or any other human-readable programminglanguage or environment, alone or in combination with scripts inJAVASCRIPT, other scripting languages and other programming source text.The term “pages” is intended to refer broadly to any region within mainmemory and the specific terminology used in a system may vary dependingon the memory architecture or processor architecture. In anotherembodiment, each of the components in the server 170 also may representone or more files or projects of source code that are digitally storedin a mass storage device such as non-volatile RAM or disk storage, inthe agricultural intelligence computer system 130 or a separaterepository system, which when compiled or interpreted cause generatingexecutable instructions which when executed cause the agriculturalintelligence computing system to perform the functions or operationsthat are described herein with reference to those modules. In otherwords, the drawing figure may represent the manner in which programmersor software developers organize and arrange source code for latercompilation into an executable, or interpretation into bytecode or theequivalent, for execution by the agricultural intelligence computersystem 130.

Hardware/virtualization layer 150 comprises one or more centralprocessing units (CPUs), memory controllers, and other devices,components, or elements of a computer system such as volatile ornon-volatile memory, non-volatile storage such as disk, and I/O devicesor interfaces as illustrated and described, for example, in connectionwith FIG. 4. The layer 150 also may comprise programmed instructionsthat are configured to support virtualization, containerization, orother technologies.

For purposes of illustrating a clear example, FIG. 1 shows a limitednumber of instances of certain functional elements. However, in otherembodiments, there may be any number of such elements. For example,embodiments may use thousands or millions of different mobile computingdevices 104 associated with different users. Further, the system 130and/or external data server computer 108 may be implemented using two ormore processors, cores, clusters, or instances of physical machines orvirtual machines, configured in a discrete location or co-located withother elements in a datacenter, shared computing facility or cloudcomputing facility.

2.2. Application Program Overview

In an embodiment, the implementation of the functions described hereinusing one or more computer programs or other software elements that areloaded into and executed using one or more general-purpose computerswill cause the general-purpose computers to be configured as aparticular machine or as a computer that is specially adapted to performthe functions described herein. Further, each of the flow diagrams thatare described further herein may serve, alone or in combination with thedescriptions of processes and functions in prose herein, as algorithms,plans or directions that may be used to program a computer or logic toimplement the functions that are described. In other words, all theprose text herein, and all the drawing figures, together are intended toprovide disclosure of algorithms, plans or directions that aresufficient to permit a skilled person to program a computer to performthe functions that are described herein, in combination with the skilland knowledge of such a person given the level of skill that isappropriate for inventions and disclosures of this type.

In an embodiment, user 102 interacts with agricultural intelligencecomputer system 130 using field manager computing device 104 configuredwith an operating system and one or more application programs or apps;the field manager computing device 104 also may interoperate with theagricultural intelligence computer system independently andautomatically under program control or logical control and direct userinteraction is not always required. Field manager computing device 104broadly represents one or more of a smart phone, PDA, tablet computingdevice, laptop computer, desktop computer, workstation, or any othercomputing device capable of transmitting and receiving information andperforming the functions described herein. Field manager computingdevice 104 may communicate via a network using a mobile applicationstored on field manager computing device 104, and in some embodiments,the device may be coupled using a cable 113 or connector to the sensor112 and/or controller 114. A particular user 102 may own, operate orpossess and use, in connection with system 130, more than one fieldmanager computing device 104 at a time.

The mobile application may provide client-side functionality, via thenetwork to one or more mobile computing devices. In an exampleembodiment, field manager computing device 104 may access the mobileapplication via a web browser or a local client application or app.Field manager computing device 104 may transmit data to, and receivedata from, one or more front-end servers, using web-based protocols orformats such as HTTP, XML and/or JSON, or app-specific protocols. In anexample embodiment, the data may take the form of requests and userinformation input, such as field data, into the mobile computing device.In some embodiments, the mobile application interacts with locationtracking hardware and software on field manager computing device 104which determines the location of field manager computing device 104using standard tracking techniques such as multilateration of radiosignals, the global positioning system (GPS), WiFi positioning systems,or other methods of mobile positioning. In some cases, location data orother data associated with the device 104, user 102, and/or useraccount(s) may be obtained by queries to an operating system of thedevice or by requesting an app on the device to obtain data from theoperating system.

In an embodiment, field manager computing device 104 sends field data106 to agricultural intelligence computer system 130 comprising orincluding, but not limited to, data values representing one or more of:a geographical location of the one or more fields, tillage informationfor the one or more fields, crops planted in the one or more fields, andsoil data extracted from the one or more fields. Field manager computingdevice 104 may send field data 106 in response to user input from user102 specifying the data values for the one or more fields. Additionally,field manager computing device 104 may automatically send field data 106when one or more of the data values becomes available to field managercomputing device 104. For example, field manager computing device 104may be communicatively coupled to remote sensor 112 and/or applicationcontroller 114. In response to receiving data indicating thatapplication controller 114 released water onto the one or more fields,field manager computing device 104 may send field data 106 toagricultural intelligence computer system 130 indicating that water wasreleased on the one or more fields. Field data 106 identified in thisdisclosure may be input and communicated using electronic digital datathat is communicated between computing devices using parameterized URLsover HTTP, or another suitable communication or messaging protocol.

A commercial example of the mobile application is CLIMATE FIELDVIEW,commercially available from The Climate Corporation, San Francisco,Calif. The CLIMATE FIELDVIEW application, or other applications, may bemodified, extended, or adapted to include features, functions, andprogramming that have not been disclosed earlier than the filing date ofthis disclosure. In one embodiment, the mobile application comprises anintegrated software platform that allows a grower to make fact-baseddecisions for their operation because it combines historical data aboutthe grower's fields with any other data that the grower wishes tocompare. The combinations and comparisons may be performed in real timeand are based upon scientific models that provide potential scenarios topermit the grower to make better, more informed decisions.

FIG. 2 illustrates two views of an example logical organization of setsof instructions in main memory when an example mobile application isloaded for execution. In FIG. 2, each named element represents a regionof one or more pages of RAM or other main memory, or one or more blocksof disk storage or other non-volatile storage, and the programmedinstructions within those regions. In one embodiment, in view (a), amobile computer application 200 comprises account-fields-dataingestion-sharing instructions 202, overview and alert instructions 204,digital map book instructions 206, seeds and planting instructions 208,nitrogen instructions 210, weather instructions 212, field healthinstructions 214, and performance instructions 216.

In one embodiment, a mobile computer application 200 comprisesaccount-fields-data ingestion-sharing instructions 202 which areprogrammed to receive, translate, and ingest field data from third partysystems via manual upload or APIs. Data types may include fieldboundaries, yield maps, as-planted maps, soil test results, as-appliedmaps, and/or management zones, among others. Data formats may includeshape files, native data formats of third parties, and/or farmmanagement information system (FMIS) exports, among others. Receivingdata may occur via manual upload, e-mail with attachment, external APIsthat push data to the mobile application, or instructions that call APIsof external systems to pull data into the mobile application. In oneembodiment, mobile computer application 200 comprises a data inbox. Inresponse to receiving a selection of the data inbox, the mobile computerapplication 200 may display a graphical user interface for manuallyuploading data files and importing uploaded files to a data manager.

In one embodiment, digital map book instructions 206 comprise field mapdata layers stored in device memory and are programmed with datavisualization tools and geospatial field notes. This provides growerswith convenient information close at hand for reference, logging andvisual insights into field performance. In one embodiment, overview andalert instructions 204 are programmed to provide an operation-wide viewof what is important to the grower, and timely recommendations to takeaction or focus on particular issues. This permits the grower to focustime on what needs attention, to save time and preserve yield throughoutthe season. In one embodiment, seeds and planting instructions 208 areprogrammed to provide tools for seed selection, hybrid placement, andscript creation, including variable rate (VR) script creation, basedupon scientific models and empirical data. This enables growers tomaximize yield or return on investment through optimized seed purchase,placement and population.

In one embodiment, script generation instructions 205 are programmed toprovide an interface for generating scripts, including variable rate(VR) fertility scripts. The interface enables growers to create scriptsfor field implements, such as nutrient applications, planting, andirrigation. For example, a planting script interface may comprise toolsfor identifying a type of seed for planting. Upon receiving a selectionof the seed type, mobile computer application 200 may display one ormore fields broken into management zones, such as the field map datalayers created as part of digital map book instructions 206. In oneembodiment, the management zones comprise soil zones along with a panelidentifying each soil zone and a soil name, texture, drainage for eachzone, or other field data. Mobile computer application 200 may alsodisplay tools for editing or creating such, such as graphical tools fordrawing management zones, such as soil zones, over a map of one or morefields. Planting procedures may be applied to all management zones ordifferent planting procedures may be applied to different subsets ofmanagement zones. When a script is created, mobile computer application200 may make the script available for download in a format readable byan application controller, such as an archived or compressed format.Additionally, and/or alternatively, a script may be sent directly to cabcomputer 115 from mobile computer application 200 and/or uploaded to oneor more data servers and stored for further use.

In one embodiment, nitrogen instructions 210 are programmed to providetools to inform nitrogen decisions by visualizing the availability ofnitrogen to crops. This enables growers to maximize yield or return oninvestment through optimized nitrogen application during the season.Example programmed functions include displaying images such as SSURGOimages to enable drawing of application zones and/or images generatedfrom subfield soil data, such as data obtained from sensors, at a highspatial resolution (as fine as 10 meters or smaller because of theirproximity to the soil); upload of existing grower-defined zones;providing an application graph and/or a map to enable tuningapplication(s) of nitrogen across multiple zones; output of scripts todrive machinery; tools for mass data entry and adjustment; and/or mapsfor data visualization, among others. “Mass data entry,” in thiscontext, may mean entering data once and then applying the same data tomultiple fields that have been defined in the system; example data mayinclude nitrogen application data that is the same for many fields ofthe same grower, but such mass data entry applies to the entry of anytype of field data into the mobile computer application 200. Forexample, nitrogen instructions 210 may be programmed to acceptdefinitions of nitrogen planting and practices programs and to acceptuser input specifying to apply those programs across multiple fields.“Nitrogen planting programs,” in this context, refers to a stored, namedset of data that associates: a name, color code or other identifier, oneor more dates of application, types of material or product for each ofthe dates and amounts, method of application or incorporation such asinjected or knifed in, and/or amounts or rates of application for eachof the dates, crop or hybrid that is the subject of the application,among others. “Nitrogen practices programs,” in this context, refers toa stored, named set of data that associates: a practices name; aprevious crop; a tillage system; a date of primarily tillage; one ormore previous tillage systems that were used; one or more indicators ofapplication type, such as manure, that were used. Nitrogen instructions210 also may be programmed to generate and cause displaying a nitrogengraph, which indicates projections of plant use of the specifiednitrogen and whether a surplus or shortfall is predicted; in someembodiments, different color indicators may signal a magnitude ofsurplus or magnitude of shortfall. In one embodiment, a nitrogen graphcomprises a graphical display in a computer display device comprising aplurality of rows, each row associated with and identifying a field;data specifying what crop is planted in the field, the field size, thefield location, and a graphic representation of the field perimeter; ineach row, a timeline by month with graphic indicators specifying eachnitrogen application and amount at points correlated to month names; andnumeric and/or colored indicators of surplus or shortfall, in whichcolor indicates magnitude.

In one embodiment, the nitrogen graph may include one or more user inputfeatures, such as dials or slider bars, to dynamically change thenitrogen planting and practices programs so that a user may optimize hisnitrogen graph. The user may then use his optimized nitrogen graph andthe related nitrogen planting and practices programs to implement one ormore scripts, including variable rate (VR) fertility scripts. Nitrogeninstructions 210 also may be programmed to generate and cause displayinga nitrogen map, which indicates projections of plant use of thespecified nitrogen and whether a surplus or shortfall is predicted; insome embodiments, different color indicators may signal a magnitude ofsurplus or magnitude of shortfall. The nitrogen map may displayprojections of plant use of the specified nitrogen and whether a surplusor shortfall is predicted for different times in the past and the future(such as daily, weekly, monthly or yearly) using numeric and/or coloredindicators of surplus or shortfall, in which color indicates magnitude.In one embodiment, the nitrogen map may include one or more user inputfeatures, such as dials or slider bars, to dynamically change thenitrogen planting and practices programs so that a user may optimize hisnitrogen map, such as to obtain a preferred amount of surplus toshortfall. The user may then use his optimized nitrogen map and therelated nitrogen planting and practices programs to implement one ormore scripts, including variable rate (VR) fertility scripts. In otherembodiments, similar instructions to the nitrogen instructions 210 couldbe used for application of other nutrients (such as phosphorus andpotassium) application of pesticide, and irrigation programs.

In one embodiment, weather instructions 212 are programmed to providefield-specific recent weather data and forecasted weather information.This enables growers to save time and have an efficient integrateddisplay with respect to daily operational decisions.

In one embodiment, field health instructions 214 are programmed toprovide timely remote sensing images highlighting in-season cropvariation and potential concerns. Example programmed functions includecloud checking, to identify possible clouds or cloud shadows;determining nitrogen indices based on field images; graphicalvisualization of scouting layers, including, for example, those relatedto field health, and viewing and/or sharing of scouting notes; and/ordownloading satellite images from multiple sources and prioritizing theimages for the grower, among others.

In one embodiment, performance instructions 216 are programmed toprovide reports, analysis, and insight tools using on-farm data forevaluation, insights and decisions. This enables the grower to seekimproved outcomes for the next year through fact-based conclusions aboutwhy return on investment was at prior levels, and insight intoyield-limiting factors. The performance instructions 216 may beprogrammed to communicate via the network(s) 109 to back-end analyticsprograms executed at agricultural intelligence computer system 130and/or external data server computer 108 and configured to analyzemetrics such as yield, hybrid, population, SSURGO, soil tests, orelevation, among others. Programmed reports and analysis may includeyield variability analysis, benchmarking of yield and other metricsagainst other growers based on anonymized data collected from manygrowers, or data for seeds and planting, among others.

Applications having instructions configured in this way may beimplemented for different computing device platforms while retaining thesame general user interface appearance. For example, the mobileapplication may be programmed for execution on tablets, smartphones, orserver computers that are accessed using browsers at client computers.Further, the mobile application as configured for tablet computers orsmartphones may provide a full app experience or a cab app experiencethat is suitable for the display and processing capabilities of cabcomputer 115. For example, referring now to view (b) of FIG. 2, in oneembodiment a cab computer application 220 may comprise maps-cabinstructions 222, remote view instructions 224, data collect andtransfer instructions 226, machine alerts instructions 228, scripttransfer instructions 230, and scouting-cab instructions 232. The codebase for the instructions of view (b) may be the same as for view (a)and executables implementing the code may be programmed to detect thetype of platform on which they are executing and to expose, through agraphical user interface, only those functions that are appropriate to acab platform or full platform. This approach enables the system torecognize the distinctly different user experience that is appropriatefor an in-cab environment and the different technology environment ofthe cab. The maps-cab instructions 222 may be programmed to provide mapviews of fields, farms or regions that are useful in directing machineoperation. The remote view instructions 224 may be programmed to turnon, manage, and provide views of machine activity in real-time or nearreal-time to other computing devices connected to the system 130 viawireless networks, wired connectors or adapters, and the like. The datacollect and transfer instructions 226 may be programmed to turn on,manage, and provide transfer of data collected at sensors andcontrollers to the system 130 via wireless networks, wired connectors oradapters, and the like. The machine alerts instructions 228 may beprogrammed to detect issues with operations of the machine or tools thatare associated with the cab and generate operator alerts. The scripttransfer instructions 230 may be configured to transfer in scripts ofinstructions that are configured to direct machine operations or thecollection of data. The scouting-cab instructions 230 may be programmedto display location-based alerts and information received from thesystem 130 based on the location of the agricultural apparatus 111 orsensors 112 in the field and ingest, manage, and provide transfer oflocation-based scouting observations to the system 130 based on thelocation of the agricultural apparatus 111 or sensors 112 in the field.

2.3. Data Ingest to the Computer System

In an embodiment, external data server computer 108 stores external data110, including soil data representing soil composition for the one ormore fields and weather data representing temperature and precipitationon the one or more fields. The weather data may include past and presentweather data as well as forecasts for future weather data. In anembodiment, external data server computer 108 comprises a plurality ofservers hosted by different entities. For example, a first server maycontain soil composition data while a second server may include weatherdata. Additionally, soil composition data may be stored in multipleservers. For example, one server may store data representing percentageof sand, silt, and clay in the soil while a second server may store datarepresenting percentage of organic matter (OM) in the soil.

In an embodiment, remote sensor 112 comprises one or more sensors thatare programmed or configured to produce one or more observations. Remotesensor 112 may be aerial sensors, such as satellites, vehicle sensors,planting equipment sensors, tillage sensors, fertilizer or insecticideapplication sensors, harvester sensors, and any other implement capableof receiving data from the one or more fields. In an embodiment,application controller 114 is programmed or configured to receiveinstructions from agricultural intelligence computer system 130.Application controller 114 may also be programmed or configured tocontrol an operating parameter of an agricultural vehicle or implement.For example, an application controller may be programmed or configuredto control an operating parameter of a vehicle, such as a tractor,planting equipment, tillage equipment, fertilizer or insecticideequipment, harvester equipment, or other farm implements such as a watervalve. Other embodiments may use any combination of sensors andcontrollers, of which the following are merely selected examples.

The system 130 may obtain or ingest data under user 102 control, on amass basis from a large number of growers who have contributed data to ashared database system. This form of obtaining data may be termed“manual data ingest” as one or more user-controlled computer operationsare requested or triggered to obtain data for use by the system 130. Asan example, the CLIMATE FIELDVIEW application, commercially availablefrom The Climate Corporation, San Francisco, Calif., may be operated toexport data to system 130 for storing in the repository 160.

For example, seed monitor systems can both control planter apparatuscomponents and obtain planting data, including signals from seed sensorsvia a signal harness that comprises a CAN backbone and point-to-pointconnections for registration and/or diagnostics. Seed monitor systemscan be programmed or configured to display seed spacing, population andother information to the user via the cab computer 115 or other deviceswithin the system 130. Examples are disclosed in U.S. Pat. No. 8,738,243and US Pat. Pub. 20150094916, and the present disclosure assumesknowledge of those other patent disclosures.

Likewise, yield monitor systems may contain yield sensors for harvesterapparatus that send yield measurement data to the cab computer 115 orother devices within the system 130. Yield monitor systems may utilizeone or more remote sensors 112 to obtain grain moisture measurements ina combine or other harvester and transmit these measurements to the uservia the cab computer 115 or other devices within the system 130.

In an embodiment, examples of sensors 112 that may be used with anymoving vehicle or apparatus of the type described elsewhere hereininclude kinematic sensors and position sensors. Kinematic sensors maycomprise any of speed sensors such as radar or wheel speed sensors,accelerometers, or gyros. Position sensors may comprise GPS receivers ortransceivers, or WiFi-based position or mapping apps that are programmedto determine location based upon nearby WiFi hotspots, among others.

In an embodiment, examples of sensors 112 that may be used with tractorsor other moving vehicles include engine speed sensors, fuel consumptionsensors, area counters or distance counters that interact with GPS orradar signals, PTO (power take-off) speed sensors, tractor hydraulicssensors configured to detect hydraulics parameters such as pressure orflow, and/or and hydraulic pump speed, wheel speed sensors or wheelslippage sensors. In an embodiment, examples of controllers 114 that maybe used with tractors include hydraulic directional controllers,pressure controllers, and/or flow controllers; hydraulic pump speedcontrollers; speed controllers or governors; hitch position controllers;or wheel position controllers provide automatic steering.

In an embodiment, examples of sensors 112 that may be used with seedplanting equipment such as planters, drills, or air seeders include seedsensors, which may be optical, electromagnetic, or impact sensors;downforce sensors such as load pins, load cells, pressure sensors; soilproperty sensors such as reflectivity sensors, moisture sensors,electrical conductivity sensors, optical residue sensors, or temperaturesensors; component operating criteria sensors such as planting depthsensors, downforce cylinder pressure sensors, seed disc speed sensors,seed drive motor encoders, seed conveyor system speed sensors, or vacuumlevel sensors; or pesticide application sensors such as optical or otherelectromagnetic sensors, or impact sensors. In an embodiment, examplesof controllers 114 that may be used with such seed planting equipmentinclude: toolbar fold controllers, such as controllers for valvesassociated with hydraulic cylinders; downforce controllers, such ascontrollers for valves associated with pneumatic cylinders, airbags, orhydraulic cylinders, and programmed for applying downforce to individualrow units or an entire planter frame; planting depth controllers, suchas linear actuators; metering controllers, such as electric seed meterdrive motors, hydraulic seed meter drive motors, or swath controlclutches; hybrid selection controllers, such as seed meter drive motors,or other actuators programmed for selectively allowing or preventingseed or an air-seed mixture from delivering seed to or from seed metersor central bulk hoppers; metering controllers, such as electric seedmeter drive motors, or hydraulic seed meter drive motors; seed conveyorsystem controllers, such as controllers for a belt seed deliveryconveyor motor; marker controllers, such as a controller for a pneumaticor hydraulic actuator; or pesticide application rate controllers, suchas metering drive controllers, orifice size or position controllers.

In an embodiment, examples of sensors 112 that may be used with tillageequipment include position sensors for tools such as shanks or discs;tool position sensors for such tools that are configured to detectdepth, gang angle, or lateral spacing; downforce sensors; or draft forcesensors. In an embodiment, examples of controllers 114 that may be usedwith tillage equipment include downforce controllers or tool positioncontrollers, such as controllers configured to control tool depth, gangangle, or lateral spacing.

In an embodiment, examples of sensors 112 that may be used in relationto apparatus for applying fertilizer, insecticide, fungicide and thelike, such as on-planter starter fertilizer systems, subsoil fertilizerapplicators, or fertilizer sprayers, include: fluid system criteriasensors, such as flow sensors or pressure sensors; sensors indicatingwhich spray head valves or fluid line valves are open; sensorsassociated with tanks, such as fill level sensors; sectional orsystem-wide supply line sensors, or row-specific supply line sensors; orkinematic sensors such as accelerometers disposed on sprayer booms. Inan embodiment, examples of controllers 114 that may be used with suchapparatus include pump speed controllers; valve controllers that areprogrammed to control pressure, flow, direction, PWM and the like; orposition actuators, such as for boom height, subsoiler depth, or boomposition.

In an embodiment, examples of sensors 112 that may be used withharvesters include yield monitors, such as impact plate strain gauges orposition sensors, capacitive flow sensors, load sensors, weight sensors,or torque sensors associated with elevators or augers, or optical orother electromagnetic grain height sensors; grain moisture sensors, suchas capacitive sensors; grain loss sensors, including impact, optical, orcapacitive sensors; header operating criteria sensors such as headerheight, header type, deck plate gap, feeder speed, and reel speedsensors; separator operating criteria sensors, such as concaveclearance, rotor speed, shoe clearance, or chaffer clearance sensors;auger sensors for position, operation, or speed; or engine speedsensors. In an embodiment, examples of controllers 114 that may be usedwith harvesters include header operating criteria controllers forelements such as header height, header type, deck plate gap, feederspeed, or reel speed; separator operating criteria controllers forfeatures such as concave clearance, rotor speed, shoe clearance, orchaffer clearance; or controllers for auger position, operation, orspeed.

In an embodiment, examples of sensors 112 that may be used with graincarts include weight sensors, or sensors for auger position, operation,or speed. In an embodiment, examples of controllers 114 that may be usedwith grain carts include controllers for auger position, operation, orspeed.

In an embodiment, examples of sensors 112 and controllers 114 may beinstalled in unmanned aerial vehicle (UAV) apparatus or “drones.” Suchsensors may include cameras with detectors effective for any range ofthe electromagnetic spectrum including visible light, infrared,ultraviolet, near-infrared (NIR), and the like; accelerometers;altimeters; temperature sensors; humidity sensors; pitot tube sensors orother airspeed or wind velocity sensors; battery life sensors; or radaremitters and reflected radar energy detection apparatus. Suchcontrollers may include guidance or motor control apparatus, controlsurface controllers, camera controllers, or controllers programmed toturn on, operate, obtain data from, manage and configure any of theforegoing sensors. Examples are disclosed in U.S. patent applicationSer. No. 14/831,165 and the present disclosure assumes knowledge of thatother patent disclosure.

In an embodiment, sensors 112 and controllers 114 may be affixed to soilsampling and measurement apparatus that is configured or programmed tosample soil and perform soil chemistry tests, soil moisture tests, andother tests pertaining to soil. For example, the apparatus disclosed inU.S. Pat. Nos. 8,767,194 and 8,712,148 may be used, and the presentdisclosure assumes knowledge of those patent disclosures.

In an embodiment, sensors 112 and controllers 114 may comprise weatherdevices for monitoring weather conditions of fields. For example, theapparatus disclosed in U.S. Provisional Application No. 62/154,207,filed on Apr. 29, 2015, U.S. Provisional Application No. 62/175,160,filed on Jun. 12, 2015, U.S. Provisional Application No. 62/198,060,filed on Jul. 28, 2015, and U.S. Provisional Application No. 62/220,852,filed on Sep. 18, 2015, may be used, and the present disclosure assumesknowledge of those patent disclosures.

2.4 Process Overview-Agronomic Model Training

In an embodiment, the agricultural intelligence computer system 130 isprogrammed or configured to create an agronomic model. In this context,an agronomic model is a data structure in memory of the agriculturalintelligence computer system 130 that comprises field data 106, such asidentification data and harvest data for one or more fields. Theagronomic model may also comprise calculated agronomic properties whichdescribe either conditions which may affect the growth of one or morecrops on a field, or properties of the one or more crops, or both.Additionally, an agronomic model may comprise recommendations based onagronomic factors such as crop recommendations, irrigationrecommendations, planting recommendations, and harvestingrecommendations. The agronomic factors may also be used to estimate oneor more crop related results, such as agronomic yield. The agronomicyield of a crop is an estimate of quantity of the crop that is produced,or in some examples the revenue or profit obtained from the producedcrop.

In an embodiment, the agricultural intelligence computer system 130 mayuse a preconfigured agronomic model to calculate agronomic propertiesrelated to currently received location and crop information for one ormore fields. The preconfigured agronomic model is based upon previouslyprocessed field data, including but not limited to, identification data,harvest data, fertilizer data, and weather data. The preconfiguredagronomic model may have been cross validated to ensure accuracy of themodel. Cross validation may include comparison to ground truthing thatcompares predicted results with actual results on a field, such as acomparison of precipitation estimate with a rain gauge or sensorproviding weather data at the same or nearby location or an estimate ofnitrogen content with a soil sample measurement.

FIG. 3 illustrates a programmed process by which the agriculturalintelligence computer system generates one or more preconfiguredagronomic models using field data provided by one or more data sources.FIG. 3 may serve as an algorithm or instructions for programming thefunctional elements of the agricultural intelligence computer system 130to perform the operations that are now described.

At block 305, the agricultural intelligence computer system 130 isconfigured or programmed to implement agronomic data preprocessing offield data received from one or more data sources. The field datareceived from one or more data sources may be preprocessed for thepurpose of removing noise and distorting effects within the agronomicdata including measured outliers that would bias received field datavalues. Embodiments of agronomic data preprocessing may include, but arenot limited to, removing data values commonly associated with outlierdata values, specific measured data points that are known tounnecessarily skew other data values, data smoothing techniques used toremove or reduce additive or multiplicative effects from noise, andother filtering or data derivation techniques used to provide cleardistinctions between positive and negative data inputs.

At block 310, the agricultural intelligence computer system 130 isconfigured or programmed to perform data subset selection using thepreprocessed field data in order to identify datasets useful for initialagronomic model generation. The agricultural intelligence computersystem 130 may implement data subset selection techniques including, butnot limited to, a genetic algorithm method, an all subset models method,a sequential search method, a stepwise regression method, a particleswarm optimization method, and an ant colony optimization method. Forexample, a genetic algorithm selection technique uses an adaptiveheuristic search algorithm, based on evolutionary principles of naturalselection and genetics, to determine and evaluate datasets within thepreprocessed agronomic data.

At block 315, the agricultural intelligence computer system 130 isconfigured or programmed to implement field dataset evaluation. In anembodiment, a specific field dataset is evaluated by creating anagronomic model and using specific quality thresholds for the createdagronomic model. Agronomic models may be compared using cross validationtechniques including, but not limited to, root mean square error ofleave-one-out cross validation (RMSECV), mean absolute error, and meanpercentage error. For example, RMSECV can cross validate agronomicmodels by comparing predicted agronomic property values created by theagronomic model against historical agronomic property values collectedand analyzed. In an embodiment, the agronomic dataset evaluation logicis used as a feedback loop where agronomic datasets that do not meetconfigured quality thresholds are used during future data subsetselection steps (block 310).

At block 320, the agricultural intelligence computer system 130 isconfigured or programmed to implement agronomic model creation basedupon the cross validated agronomic datasets. In an embodiment, agronomicmodel creation may implement multivariate regression techniques tocreate preconfigured agronomic data models.

At block 325, the agricultural intelligence computer system 130 isconfigured or programmed to store the preconfigured agronomic datamodels for future field data evaluation.

2.5 Disease Recognition and Yield Estimation

In some embodiments, the agricultural intelligence computer system 130includes a disease recognition and yield estimation server (“theserver”) 170. The server 170 comprises an account management component172, a mobile device interface 174, a model management component 176,and an app management component 178.

In some embodiments, the account management component 172 is programmedto maintain accounts corresponding to users or client computers. Datagenerated by the server or a client computer for an account can bestored under the account and made available to any users having accessto the account. The model management component 176 is programmed tocreate and update a first model or technique for recognizing diseasesand crop types as sets of computer-executable instructions. The firstmodel generally accepts an image as input and produces an identificationof a disease or a crop type and related information as output. The modelmanagement component 176 is also programmed to create and update asecond model or technique for estimating yield for a crop and for afield of crops as sets of computer-executable instructions. The secondmodel generally accepts an image as input and produces a yield amountfor the crop and a corresponding yield amount for a field of crops.

In some embodiments, the app management component 178 is programmed tocreate and update a computer program having computer-executableinstructions, such as a mobile app, that enables a client computer tomanage a crop field evaluation process. The computer program can managea graphical user interface (“GUI”) coupled with a backend engine toprocess input and output data associated with the GUI. The backendengine could invoke the first model for recognizing diseases and croptypes or the second model for estimating crop yield for a crop field.The mobile device interface 174 is configured to communicate with aclient computer over a communication network, through the communicationlayer 132. The communication can include receiving a request for thecomputer program from a client computer, transmitting the computerprogram to a client computer, receiving account data from a clientcomputer, or sending account data to a client computer. Account data caninclude data provided by a user of a client computer or automaticallygenerated by a client computer.

FIG. 7 illustrates example components of a client computer. The clientcomputer 700 can be field manager computing device 104, cab computer115, or any other mobile device. The client computer 700 can be anintegrated device that includes an image capturing device, such as acamera, or a display device, such as a screen. The client computer canalso communicate with external image capturing or display devicesthrough its networking capabilities.

In some embodiments, the client computer 700 can store a crop analysisapplication 710, which can be the computer program developed by andreceived from the server. The client computer or the crop analysisapplication 710 can comprise a server interface 702, a diseasemanagement component 704, a yield estimation component 706, and a userinterface 708. The server interface 702 is programmed to communicatewith the server over a communication network, such as a cellularnetwork. The communication can include sending a request for a computerprogram that enables the client computer to manage a crop fieldevaluation process to the server, receiving the computer program fromthe server, sending account data, such as data provided by a user orautomatically generated by the client computer, to the server, orreceiving account data, such as data previously provided by a user, fromthe server. The user interface 708 is programmed to manage a GUI thatenables a user to capture the surroundings in a stream of images,coordinates with the disease management component 704 and the yieldestimation component 706 which analyze the images, and displays theanalysis results. The disease management component 704 is configured torecognize predetermined diseases or crop types from the images. Theyield estimation component 706 is programmed to compute a crop yield fora crop, such as a kernel count for a corn ear, and further calculate acrop yield over a crop field.

In some embodiments, the client computer 700 can further comprise an appdata storage 750, including a database 720, for storing data associatedwith the crop analysis application 710. Data stored in the app storage750 can include some data originally stored in the model data field datarepository 160 and transmitted by the server, such as the number ofcrops in a field or the crop density in a field. Information regardingspecific crop diseases, in terms of categories, causes, symptoms, ortreatments, can also be stored in the app storage 750. In addition,images analyzed through the crop analysis application 710 and results ofthe analysis can be stored in the app storage 750.

In some embodiments, the client computer 700 can further comprise anoperating system 740 which offer OS services 742. For example, iOSsupports AVCam-iOS as a set of application programming interfaces forworking with many types of photos and images. Such OS primitives orservices can be utilized to execute the crop analysis application 710efficiently.

FIG. 1 and FIG. 7 illustrate examples only and the agriculturalintelligence computer system 130 and the client computer can comprisefewer or more functional or storage components. Each of the functionalcomponents can be implemented as software components, general orspecific-purpose hardware components, firmware components, or anycombination thereof. A storage component can be implemented using any ofrelational databases, object databases, flat file systems, or JSONstores. A storage component can be connected to the functionalcomponents locally or through the networks using programmatic calls,remote procedure call (RPC) facilities or a messaging bus. A componentmay or may not be self-contained. Depending upon implementation-specificor other considerations, the components may be centralized ordistributed functionally or physically. Some of the components thatreside in the server in these examples can reside in the clientcomputer, and vice versa.

2.6 Implementation Example—Hardware Overview

According to one embodiment, the techniques described herein areimplemented by one or more special-purpose computing devices. Thespecial-purpose computing devices may be hard-wired to perform thetechniques, or may include digital electronic devices such as one ormore application-specific integrated circuits (ASICs) or fieldprogrammable gate arrays (FPGAs) that are persistently programmed toperform the techniques, or may include one or more general purposehardware processors programmed to perform the techniques pursuant toprogram instructions in firmware, memory, other storage, or acombination. Such special-purpose computing devices may also combinecustom hard-wired logic, ASICs, or FPGAs with custom programming toaccomplish the techniques. The special-purpose computing devices may bedesktop computer systems, portable computer systems, handheld devices,networking devices or any other device that incorporates hard-wiredand/or program logic to implement the techniques.

For example, FIG. 4 is a block diagram that illustrates a computersystem 400 upon which an embodiment of the invention may be implemented.Computer system 400 includes a bus 402 or other communication mechanismfor communicating information, and a hardware processor 404 coupled withbus 402 for processing information. Hardware processor 404 may be, forexample, a general purpose microprocessor.

Computer system 400 also includes a main memory 406, such as a randomaccess memory (RAM) or other dynamic storage device, coupled to bus 402for storing information and instructions to be executed by processor404. Main memory 406 also may be used for storing temporary variables orother intermediate information during execution of instructions to beexecuted by processor 404. Such instructions, when stored innon-transitory storage media accessible to processor 404, rendercomputer system 400 into a special-purpose machine that is customized toperform the operations specified in the instructions.

Computer system 400 further includes a read only memory (ROM) 408 orother static storage device coupled to bus 402 for storing staticinformation and instructions for processor 404. A storage device 410,such as a magnetic disk, optical disk, or solid-state drive is providedand coupled to bus 402 for storing information and instructions.

Computer system 400 may be coupled via bus 402 to a display 412, such asa cathode ray tube (CRT), for displaying information to a computer user.An input device 414, including alphanumeric and other keys, is coupledto bus 402 for communicating information and command selections toprocessor 404. Another type of user input device is cursor control 416,such as a mouse, a trackball, or cursor direction keys for communicatingdirection information and command selections to processor 404 and forcontrolling cursor movement on display 412. This input device typicallyhas two degrees of freedom in two axes, a first axis (e.g., x) and asecond axis (e.g., y), that allows the device to specify positions in aplane.

Computer system 400 may implement the techniques described herein usingcustomized hard-wired logic, one or more ASICs or FPGAs, firmware and/orprogram logic which in combination with the computer system causes orprograms computer system 400 to be a special-purpose machine. Accordingto one embodiment, the techniques herein are performed by computersystem 400 in response to processor 404 executing one or more sequencesof one or more instructions contained in main memory 406. Suchinstructions may be read into main memory 406 from another storagemedium, such as storage device 410. Execution of the sequences ofinstructions contained in main memory 406 causes processor 404 toperform the process steps described herein. In alternative embodiments,hard-wired circuitry may be used in place of or in combination withsoftware instructions.

The term “storage media” as used herein refers to any non-transitorymedia that store data and/or instructions that cause a machine tooperate in a specific fashion. Such storage media may comprisenon-volatile media and/or volatile media. Non-volatile media includes,for example, optical disks, magnetic disks, or solid-state drives, suchas storage device 410. Volatile media includes dynamic memory, such asmain memory 406. Common forms of storage media include, for example, afloppy disk, a flexible disk, hard disk, solid-state drive, magnetictape, or any other magnetic data storage medium, a CD-ROM, any otheroptical data storage medium, any physical medium with patterns of holes,a RAM, a PROM, and EPROM, a FLASH-EPROM, NVRAM, any other memory chip orcartridge.

Storage media is distinct from but may be used in conjunction withtransmission media. Transmission media participates in transferringinformation between storage media. For example, transmission mediaincludes coaxial cables, copper wire and fiber optics, including thewires that comprise bus 402. Transmission media can also take the formof acoustic or light waves, such as those generated during radio-waveand infra-red data communications.

Various forms of media may be involved in carrying one or more sequencesof one or more instructions to processor 404 for execution. For example,the instructions may initially be carried on a magnetic disk orsolid-state drive of a remote computer. The remote computer can load theinstructions into its dynamic memory and send the instructions over atelephone line using a modem. A modem local to computer system 400 canreceive the data on the telephone line and use an infra-red transmitterto convert the data to an infra-red signal. An infra-red detector canreceive the data carried in the infra-red signal and appropriatecircuitry can place the data on bus 402. Bus 402 carries the data tomain memory 406, from which processor 404 retrieves and executes theinstructions. The instructions received by main memory 406 mayoptionally be stored on storage device 410 either before or afterexecution by processor 404.

Computer system 400 also includes a communication interface 418 coupledto bus 402. Communication interface 418 provides a two-way datacommunication coupling to a network link 420 that is connected to alocal network 422. For example, communication interface 418 may be anintegrated services digital network (ISDN) card, cable modem, satellitemodem, or a modem to provide a data communication connection to acorresponding type of telephone line. As another example, communicationinterface 418 may be a local area network (LAN) card to provide a datacommunication connection to a compatible LAN. Wireless links may also beimplemented. In any such implementation, communication interface 418sends and receives electrical, electromagnetic or optical signals thatcarry digital data streams representing various types of information.

Network link 420 typically provides data communication through one ormore networks to other data devices. For example, network link 420 mayprovide a connection through local network 422 to a host computer 424 orto data equipment operated by an Internet Service Provider (ISP) 426.ISP 426 in turn provides data communication services through the worldwide packet data communication network now commonly referred to as the“Internet” 428. Local network 422 and Internet 428 both use electrical,electromagnetic or optical signals that carry digital data streams. Thesignals through the various networks and the signals on network link 420and through communication interface 418, which carry the digital data toand from computer system 400, are example forms of transmission media.

Computer system 400 can send messages and receive data, includingprogram code, through the network(s), network link 420 and communicationinterface 418. In the Internet example, a server 430 might transmit arequested code for an application program through Internet 428, ISP 426,local network 422 and communication interface 418.

The received code may be executed by processor 404 as it is received,and/or stored in storage device 410, or other non-volatile storage forlater execution.

3. Functional Description

3.1 Construction of a Computer Program for Managing a Field EvaluationProcess

In some embodiments, the server is programmed to gather training imagesfor each of a group of predetermined diseases. The group of diseases caninclude grey leaf spot (“GLS”), eye spot (“EYE”), northern leaf blight(“NLB”), Stewart's Wilt, or common rust. Each training image wouldpreferably have been captured in actual crop fields and verified byexperts as depicting a single crop infected with one or more of thediseases. Each image is preferably at least 400 pixels by 400 pixels insize. A group of at least 1,000 images for each disease is alsopreferred. Sample images can be obtained from Plant Village hosted byPennsylvania State University, College of Agricultural Sciences, forexample.

3.1.1 Recognizing Disease or Crop Types

In some embodiments, the server is programmed to build a first model ortechnique as a set of computer-executable instructions for recognizingone or more diseases in an input image based on the training images. Oneexample of the first model is a convolutional neural network (“CNN”).Any existing neural network implementation, such as the Keras compatiblewith Python 2.7-3.5, can be used to implement CNN-related operations.The CNN can be built by fine-tuning an existing model with an additionaldataset, such as the group of at least 1,000 images for each of thediseases noted above. The fine tuning can include truncating the lastlayer of the existing model, using a smaller learning rate, or otherapproaches. For example, with Keras, an Inception-v3 model can bereadily fine-tuned. Specifically, to change the learning rate, the sgdfunction can be used with lr set to 0.01, decay set to 0.0005, momentumset to 0.9, and nesterov set to True. In addition, to carry out modeltraining in multiple iterations, the fit function can be invoked withbatch_size set to 64 and epochs set to 50. With a complete CNN, thefirst model can output, for each of the group of diseases, a probabilitythat the subject matter depicted in the given image is infected withthat disease.

In some embodiments, a crop type can be inferred from a disease. Forexample, the GLS is known to affect maize or corn, and thus the croptype of corn can be inferred from a disease of GLS. Therefore, the firstmodel can also be used for recognizing crop types. Alternatively, aseparate model can be built in a similar manner to specificallyrecognize crop types based on training images of different crop types.

In some embodiments, other learning techniques known to someone skilledin the art can be used for building the first model, such as decisionforests and logistic regression. Metadata of the images can also be usedin characterizing each disease, such as the location where the image wascreated and time when the image was created.

3.2 Estimating Crop Yield

In some embodiments, the server is programmed to build a second model ortechnique as a set of computer-executable instructions for calculatingcrop yield per plant and for a crop field. A crop may comprise a groupof components. For example, a corn ear may contain a group of kernels,and a citrus tree may contain a group of oranges. The number ofcomponents in such a group can be an indicator of the yield of theplant. The size the crop or the total weight of the group of componentscan be another indicator of the yield. For example, a conventionalmeasure of yield for a corn field is the number of kernels per acretimes weight per kernel at standard moisture divided by 56 pounds perbushel to give bushels per acre.

In some embodiments, to estimate the size of the group or the number ofcomponents from a generated image, the second model can comprise certainimage processing operations. Any existing image processingimplementation, such as the OpenCV library (version 3.2), can be used toimplement one or more of the image processing operations of the secondmodel. FIG. 14 illustrates an example process typically performed by aclient computer to calculate the yield of a crop, such as the number ofkernels in an ear of a corn. The process can be performed throughexecuting the second model.

In step 1402, a grey-scale image is received as input. In step 1404, theimage is enhanced in contrast, such as through contrast limited adaptivehistogram equalization (CLAHE). For example, the createCLAHE function inOpenCV can be used with clipLimit set to 2 and tileGridSize set to (8,8). Other contrast enhancement algorithms known to someone skilled inthe art can be used, such as many variants of histogram equalization,other non-linear contrast methods, and linear contrast methods.

In step 1406, the enhanced image is segmented into a foreground likelyto depict a crop, such as a corn ear and a number of kernels, and abackground. The segmentation can be based on the performance of one ormore thresholding methods. A first thresholding method can be Otsu'sthresholding. For example, the threshold function in OpenCV can be usedwith thresh set to 0, maxVal set to 255, and thresholdType set toTHRESH_BINARY_INV or THRESH_OTSU. A second thresholding method can be anadaptive thresholding method. For example, the adaptiveThresholdfunction in OpenCV can be used with maxVal set to 255, adaptiveMethodset to ADAPTIVE_THRESH_GAUSSIAN_C, blockSize set to 11, and constant Cset to 2. The segmentation can then assign a pixel of the image to theforeground when at least one of the thresholding methods returns anabove-threshold determination for the pixel. Other thresholding methodsor other types of combining thresholding methods known to someoneskilled in the art can be applied. Other segmentation or classificationmethods known to someone skilled in the art can also be applied.

FIG. 15 illustrates an example image that has been enhanced in contrastand segmented into a foreground depicting a corn ear and a background.The corn ear 1502 with one or more single kernels and multi-kernelclusters constitutes the foreground, and the rest of the image in blackconstitutes the background.

In step 1408, the segmented image is updated to remove noise pixelsresulting from the thresholding, such as through morphologicaloperations. For example, the morphologyEx function in OpenCV can be usedwith kernel (structuring element in morphological operations) set to a 1by 1 element, op set to MORPH OPEN, and iterations set to 2. Thisopening operation is typically useful for removing small objects from animage. When the image depicts a corn ear, for example, the openingoperation can help remove the slightly connected area between kernels ina multi-kernel clusters thus disconnect multi-kernel clusters to singlekernels. Other methods to smooth out thresholding results, removeoutliers, or otherwise clean up images known to someone skilled in theart can be used.

In step 1410 and step 1412, the updated image is further analyzed tocalculate the size of the group of components included in the crop, suchas the number of kernels in a corn ear. The size of the group can beestimated from the size of a representative component in the group.Determination of the size of a representative component may depend onthe crop type.

In some embodiments, for a corn ear, the size of a representative kernelcan be determined by first identifying areas in the image foregroundthat correspond to isolated kernels. Kernels that grow in adjacentpositions can be identified as isolated kernels after image processingin the previous steps manifests the separation of the kernels. Inaddition, a diminished kernel set may exist due to drought stress,insect feeding, pollen desiccation, temperature variance, kernelabortion, or other reasons. Also, some kernels might have fallen off theear while being captured in the image. In these cases, as in step 1410,the foreground can be divided into connected areas, and a connected areacan be deemed to correspond to a multi-kernel cluster when the size ofthe connected area is above a first predetermined threshold, such as 500pixels. Furthermore, a connected area can be deemed to correspond to asingle kernel when the size of the connected area does not exceed thefirst predetermined threshold and also does not fall below a secondpredetermined threshold, such as 5 pixels.

In step 1412, the sizes of the connected areas deemed to correspond tosingle kernels can be aggregated. The sizes of the connected areasdeemed to correspond to multi-kernel clusters and the aggregate size ofthe connected areas deemed to correspond to single kernels can be usedto calculate the number of kernels in each multi-kernel cluster. As theimage typically shows one half or less of a corn ear, the total numberof kernels can be roughly twice the total number of kernels depicted inthe image.

In some embodiments, a default kernel size can be used. Alternatively,the orientation of the corn as depicted in the image can be determined,and classification of a connected area into a single kernel or amulti-kernel cluster can depend on the location of the connected area.For example, the kernels near the tip of the ear may be smaller. Thesame or a similar method can be applied to another type of crop that maycontain a group of clustered but distinguishable components and isolatedcomponents, such as a pea.

When the number of crops in a field is known, the yield for the fieldcan be estimated by multiplying the yield of a crop by the number ofcrops in the field. Alternatively, the yield for the field can beestimated based on a small sample of the field that might serve as abetter representative of the field than a single crop.

In some embodiments, to estimate the size or volume of a crop, thesecond model can comprise certain image processing operations based onspecific data. As one example, the second model can assume that abackground object of a known size, such as a piece of blue cloth to becontrasted with a yellow corn, is also depicted in a generated image. Asanother example, the second model can assume that camera depthinformation associated with the generated image is available. Theestimated volume of a crop can be used to estimate the number of cropsin the field given the size of the field. The estimated volume of thecrop can also be used to estimate the weight of crop, which can becombined with the kernel count of the crop to provide an accuratecharacterization of the yield of the crop, which can then be extended tothe yield for the field.

In some embodiments, the server is programmed to prepare a computerprogram having a set of computer-executable instructions that enables aclient computer to manage a crop field evaluation process. The computerprogram can coordinate communication with various electronic components,such as an image capturing device and a display device, which might beintegrated into or reachable by a client computer. Furthermore, thecomputer program can communicate with an operating system that runs on aclient computer to efficiently handle certain image-based operations incoordinating with these electronic components. In addition, the computerprogram can manage a GUI coupled with a backend engine to process inputand output data associated with the GUI. The backend engine could invokethe first model for recognizing diseases and crop types or the secondmodel for estimating crop yield for a crop field. In general, thecomputer program or the first and second models are relatively compactand can be easily packaged into a mobile app for execution on a typicalmobile device.

In some embodiments, the server is configured to transmit the computerprogram and the models together or separately to a client computer overa communication network. The transmission can be in response to arequest from a client computer or automatically according to apredetermine schedule, such as periodically or as soon as the computerprogram is updated. The server is also programmed to receive imagesgenerated by a client computer and related data, such as results ofapplying the models to the images or user notes including expert inputon those images. The server can be further programmed to incorporatethese images and related data in updating the models.

3.2 Execution of the Computer Program for Managing a Field EvaluationProcess

In some embodiments, a client computer is programmed or configured tosend a request to the server for the computer program that enables theclient computer to manage a field evaluation process, the first modelfor recognizing diseases and crop types, and the second model forestimating crop yield for a crop field. The client computer can also beconfigured to request for the first model or the second modelseparately. The receipt can be a response to a user instruction or anautomatic transmission by the server.

In some embodiments, by executing the computer program, a clientcomputer is configured to manage a field evaluation process as describedin FIG. 8. The execution can be in response to a user instruction. FIG.8 illustrates an example process performed by a client computer, such asa mobile device, to manage a field evaluation process, such as diseaserecognition and diagnosis. In step 802, the client computer isprogrammed to cause continuous capture of the surroundings andgeneration of corresponding images via an integrated or separate imagecapturing device, such as a camera. The generated images preferably haveat least the same resolution as the training images. In step 804, theclient computer is programmed to cause continuous display of thegenerated images in real time—as soon as the images are generated—via anintegrated or separate display device, such as a screen. Typically, thedisplay rate is comparable to the generation rate, in which case all thegenerated images can be displayed in order and in real time.

In step 808, the client computer is configured to continuously processthe generated images. When the processing rate is lower than thegeneration rate, the client computer can be configured to queue up thegenerated images and process them in order or selectively. For example,the client computer can be configured to process only the newest imageat the top of the queue and remove the rest of the images from thequeue. Step 808 is broken down into steps 810, 812, 814, and 816, whichspecifically illustrate disease recognition and diagnosis. Theprocessing can also be crop yield estimation.

3.2.1 Recognizing Diseases or Crop Types

In step 810, the client computer is configured to execute the firstmodel for recognizing diseases or crop types on the image beingprocessed. The execution can occur in response to receiving an image forprocessing or a user instruction. For each image being processed, thefirst model produces a distribution of probabilities into the pluralityof predetermined diseases. For each of the plurality of predetermineddiseases, the client computer can be configured to display thecorresponding probability as a disease confidence score that the imagedepicts an occurrence of the disease. Alternatively, the client computercan send a request including the image being processed to the server forexecution of the first model on the image by the server and receive themodel execution results from the server.

In step 812, the client computer is configured to cause the displaydevice to display information regarding any recognized disease, theassociated confidence score, or other relevant data. The information canbe overlaid on the image currently being displayed. As any processingdelay is typically sufficiently small in this case, the differencebetween the processed image and the currently displayed image may not bevisible to the human eyes.

In response to the information regarding the recognized diseases andother relevant data, the user may choose to receive more informationregarding one of the recognized diseases. In step 814, the clientcomputer is thus programmed to receive a specification of one or more ofthe recognized diseases.

In step 816, the client computer is programmed to cause the displaydevice to show additional data in response to the specification. Theadditional data can also be overlaid on a currently displayed image orsimply replace that currently displayed image on the screen. Theadditional data can include descriptions of known causes, symptoms, orremedial measures for the set of diseases, or similar photos for the setof diseases.

In some embodiments, the client computer can be programmed to generatean alert when a calculated confidence score associated with a recognizeddisease is above a predetermined threshold. This alert is intended tobring to the attention of a grower or any other relevant party thepotential occurrence of a disease in a crop. The alert can be in theform of an audio signal or a visual emphasis, such as displaying thedisease name and the confidence score in bold or italics, in a largerfont, or in continuous flash. The alert can also be in the form oftransmitting a message to a remote device.

In some embodiments, the client computer can be programmed to causedisplay of directions for reaching the specific geographic locationwhere the image receiving a confidence score above the predeterminedthreshold was taken. The directions are intended to direct the attentionof a grower or any other relevant party to a specific crop that haspotentially been infected with a disease. The client computer cancontinuously monitor its geographic location via a GPS or a similardevice and cause the display device to continuously overlay thedirections on currently displayed images until the client computer'sgeographic location agrees with the specific geographic location.

In some embodiments, a user might be interested in receiving moreinformation regarding one of the displayed images. The client computercan be configured to receive a selection of a currently displayed image.In response, the client computer can be programmed to stop thecontinuous processing of generated images and focus on processing theselected image. The client computer can further be programmed to causethe display device to freeze the display until results of the processingbecome available and then overlay the processing results on the nextgenerated image. At this point, the original continuous display andprocessing of generated images can be resumed.

In some embodiments, the client computer is programmed to receivefeedback from growers, pathologists, agronomists, or other parties onthe generated images and the results of executing the models on thegenerated images. As one example, a grower as a user of the clientcomputer can indicate an agreement or disagreement with a recognizeddisease or the associated confidence score, and the client computer cansend the image, the model execution result, and the user feedback backto the server for enhancing the models. As another example, the clientcomputer can be programmed to transmit the image and the model executionresult to a remote device of a pathologist. The client computer can befurther programmed to similarly receive an agreement or disagreementwith the recognized disease or the associated confidence score andtransmit the image, the model execution result, and the user feedback tothe server. The client computer can also be configured to send theimages and the model execution results without any user feedback back tothe server when the images satisfy certain predetermined criteria, suchas being associated with confidence scores above the predeterminedthreshold. In general, the client computer does not need to store thetraining images or the images generated by the camera, thus maintaininga relatively small footprint.

In some embodiments, the client computer is not being carried through acrop field by a user. The discussion above continues to mostly apply.The client computer may be integrated into an UAV. In this case, agrower may be operating the UAV remotely through a remote device. Thedisplay device might then be separate from the client computer andinstead be integrated into the remote device. Any display delay can behandled in a similar manner as any processing delay noted above.Alternatively, the client computer may remain in a remote location andrely on an UAV as the image capturing device. Instead of causing displayof the directions to a specific geographic location where an image wastaken, the client computer can be configured to send the directions tothe UAV in accordance with the computer program received from theserver.

FIG. 9, FIG. 10, FIG. 11, FIG. 12, and FIG. 13 illustrate a GUI managedby a client computer through input and output devices regarding diseaserecognition and diagnosis. The GUI can be part of the computer programreceived from the server, such as a mobile app, as discussed above. Eachdiagram of a GUI example in this disclosure comprises an example ofoutput that one or more computers may render in response to instructionsfrom the server or contained with the computer program. The instructionsmay be in a markup language or programmatic calls to library functionsthat are executed either at the server or the other computers. The sizeof areas, regions or panels may vary in different embodiments and arenot shown to scale, or in any particular format or arrangement that isrequired. Similarly, colors, borders, typefaces and other graphicalelements may be different in other embodiments and may be specifiedusing configuration data, parameters of calls, or in the instructions.

FIG. 9 illustrates an example computer interaction device showing a GUIgenerated under program control that allows a user to invoke the diseaserecognition functionality. In an embodiment, an interaction area 900comprises a header panel 904, title panel 905, and image prompt panel918. In an embodiment, header panel 904 displays a title of a functionof the associated app which, in the example of FIG. 9, indicates thatthe interaction device is then currently accepting data relating to apin location within a field. In an embodiment, selecting a Save icon 906causes the app to save the then-currently displayed data in app storageor networked storage and selecting a Close icon 908 causes changingprogram control to an earlier state from which the field pin functionwas reached. In an embodiment, title panel 905 comprises a title field910, note field 912, photo icon 914, and advanced options button 916. Inan embodiment, selecting title field 910 causes the app to prompt theuser to provide input specifying a title, which is redisplayed in thetitle panel 905 and stored in memory. Typically, the title is a name ofthe pin in the field; an example might be “central corn sample”. In anembodiment, selecting note field 912 causes the app to receive inputspecifying a note about the associated field pin. In an embodiment,selecting the photo icon 914 causes the app to programmatically access acamera of the mobile computing device by calling an operating systemprimitive or service and then await capture of a digital image from thecamera; the captured image may be redisplayed. In an embodiment, inputselecting the advanced options button 916 causes displaying the imageprompt panel 918, which otherwise is not visible. In an embodiment, theimage prompt panel 918 is a first display in a succession of displaysand prompts that permit accessing image analysis functions relating tothe image that was captured or other images. By clicking on the Beginbutton 902, the user can invoke the disease recognition functionality,for example. The computer program can also be set up such that diseaserecognition starts immediately upon its launch.

FIG. 10 illustrates an example computer interaction device showing a GUIgenerated under program control that can provide a summary of ortutorial on how to realize the disease recognition functionality. In anembodiment, an interaction area 1000 comprises an image field 1002, atext field 1004, a stage indicator 1010, a Continue button 1006, and aCancel icon 1008. The text field 1004 and image field 1002 provideinstructions for operating a camera, such as one integrated into theclient computer, to continuously scan the surroundings and generate aseries of images depicting the surroundings. The image field 1002 candisplay a photo of an actual crop. Selecting the Cancel icon 1008 causeschanging program control to an earlier state from which the interactionarea 1000 was reached. The stage indicator 1010 shows the current stagein the summary or tutorial, such as the second page of three pages.Selecting the Continue button 1006 causes changing program control tothe next stage in the summary or tutorial, such as the third page of thethree pages.

FIG. 11 illustrates an example computer interaction device showing a GUIgenerated under program control that can display results of analyzinggenerated images and receive requests to analyze select generatedimages. In an embodiment, an interaction area 1100 comprises a diseaseimage area 1108, a Tips icon 1110, and an image capture area 1106. Asthe series of images are generated at a high generation rate, the seriesof images can be displayed at a similarly high display rate in realtime, which would look like a continuous video. The computer interactiondevice can show each generated image in the disease image area 1108 inreal time. As the series of images are generated, the series of imagescan also be processed for disease recognition in order or selectively inreal time. The computer interaction device can then show the processingresults, such as information regarding one or more recognized diseasesin a disease classification area 1102 and the corresponding one or moreconfidence scores in a confidence score area 1104, on top of thecurrently displayed image. The processing results generally indicatethat any crop depicted in the processed image may be infected with theone or more diseases for the respective confidence scores orprobabilities of infection. In this example, the currently displayedimage depicts a corn leaf, the currently processed image probablydepicts the same leaf, and that leaf is likely to have been infectedwith NLB for a probability of 68%, with EYE for a probability of 17%,and with GLS for a probability of 12%.

In one embodiment, the computer interaction device can also allow a userto select the currently displayed image, such as through a capturebutton in the image capture area 1106, to interrupt the continuousprocessing of generated images and immediately process the selectedimage. Alternatively, the disease recognition functionality does notinvolve automatically processing the series of generated images but canonly rely on a user to click on the image capture button, for example,to select an image for processing. Selecting the Tips icon 1110 cancause display of additional information, such as more basic attributesof the crop depicted in the currently displayed image, a summary of howto interpret the displayed information regarding the recognize diseasesand the confidence scores, or a summary of how to receive diseaserecognition information for the currently displayed image.

FIG. 12 illustrates an example computer interaction device showing a GUIgenerated under program control that can provide additional informationabout recognized diseases. In one embodiment, an interaction area 1200comprises a header panel 1214, a disease image area 1202, a diseaseclassification area 1204, a confidence score area 1206, a symptoms panel1208, a pathogen panel 1210, and a feedback area 1212. In an embodiment,the header panel 1214 displays a title of a function of the associatedapp which, in the example of FIG. 12, indicates that the interactiondevice is then currently displaying data relating to a disease infectedby a crop in the field. In response to a specification of one of therecognized diseases, which can result from a selection of displayedinformation regarding the specified disease overlaid on an imagedepicting the specified disease, the computer interaction device canredisplay the image or maybe a portion of the image that clearly depictsthe recognized disease in the disease image area 1202. The computerinteraction device can further overlay the information regarding thespecified disease in the disease classification area 1204 and thecorresponding confidence score in the confidence score area 1206 on thedepiction of the specified disease. In addition, the computerinteraction device can display additional data regarding the specifieddisease, such as a list of symptoms in the symptoms area 1208 or a listof pathogens involved in the pathogen in the area 1210.

In one embodiment, the computer interaction device can also allow a userto provide feedback on the automatic analysis, such as informationregarding the recognized diseases and the calculated confidence scores.By clicking on YES or NO in the feedback area 1212, the user canindicate an agreement or a disagreement with the automatic analysis. Thecomputer interaction device can also enable a user to correct theanalysis results or provide notes or additional data for the analysisresults.

FIG. 13 illustrates an example computer interaction device showing a GUIgenerated under program control that can enable a user to store orupload a generated image or provide metadata for the generated image. Inone embodiment, an interaction area 1300 comprises a header panel 1312,a disease image area 1302, a title panel 1316, a type panel 1322, acolor panel 1324, a location panel 1326, and a threats panel 1310. In anembodiment, the header panel 1312 displays a title of a function of theassociated app which, in the example of FIG. 13, indicates that theinteraction device is then currently accepting data relating to a pinlocation within a field. In an embodiment, selecting a Save icon 1308causes the app to save the then-currently displayed data in app storageor networked storage and selecting a Close icon 1314 causes changingprogram control to an earlier state from which the field pin functionwas reached. In an embodiment, in response to a selection of an imagepreviously displayed by the computer interaction device and subsequentanalysis of the selected image, the computer interaction device canredisplay the selected image or a portion of the image that clearlydepicts a recognized disease in the disease image area 1302.

In an embodiment, the title panel 1316 shows a title and comprises anote field 1318 a photo icon 1320, and an upload icon 1300. Typically,the title is a name of the pin in the field or the subject matter of theimage displayed in the disease image area 1302; an example might be“central corn sample”. In an embodiment, selecting an upload icon 1306can cause the client computer to transmit the image to the server. In anembodiment, selecting note field 1318 causes the app to receive inputspecifying a note about the associated field pin. In an embodiment,selecting the photo icon 1320 causes the app to programmatically accessa camera of the client computer by calling an operating system primitiveor service and then await capture of a digital image from the camera;the captured image may be redisplayed.

In one embodiment, the type panel 1322 can indicate a type of disease,such as seasonal or permanent, that was determined from analyzing theimage. The type panel 1322 can also allow overriding of the indicatedtype of disease or a fresh selection from a predetermined list of typesusing radio buttons 1332, check boxes, or other components. The colorpanel 1324 can indicate a color of the crop depicted in the image, suchas green or yellow, that was determined from analyzing the image. Thecolor panel 1324 can also allow overriding of the indicated color of thecrop or a fresh selection from a predetermined list of colors usingradio buttons 1336, check boxes, or other components. The location panel1326 can indicate a geographical location where the image was taken, interms of GPS coordinate, for example. The location panel 1326 can alsoaccept a selection of the setting field 1340 that cause the geographicallocation to be set to the current location as measured by a GPSintegrated into the client computer. In addition, the threats panel 1310can display additional information regarding the image, such asinformation identifying the recognized disease.

3.2.2 Estimating Crop Yield

Referring back to FIG. 8, step 808 can similarly be broken into multiplesteps for crop yield estimation. The client computer can be configuredto execute the second model for estimating crop yield on the image beingprocessed. Specifically, when the execution of the first model leads toa determination of a crop type, the crop type can be an input to thesecond model for selecting a technique specific to the crop type. Foreach image being processed, the second model can produce an estimatedyield for a crop or for a field of crops or other related data. Therelated data can include an identification of the different areas of acrop, such as the tip of a corn ear, the rest of the ear, individualkernels, or each cluster of kernels. Alternatively, the client computercan send a request including the image being processed to the server forexecution of the second model on the image by the server and receive themodel execution results from the server. The client computer can then beconfigured to cause the display device to overlay the yield informationor other related data on the image currently being displayed.

FIG. 16 and FIG. 17 illustrate a graphical user interface managed by aclient computer through input and output devices regarding crop yieldestimation. FIG. 16 illustrates an example computer interaction deviceshowing a GUI generated under program control that can provide a summaryof or tutorial on how to realize the yield estimation functionality. Inone embodiment, similar to the interaction area illustrated in FIG. 10,an interaction area 1600 includes an image field 1602, a text field1604, a stage indicator 1608, a Continue button 1606, and a Cancel icon.The text field 1604 and image field 1602 provide instructions foroperating a camera, such as one integrated into the client computer, todirect the camera to a crop, such as a corn, to generate an image of thecrop for processing. The image field 1602 can display a photo of anactual crop. When the yield estimation functionality is invoked inresponse to a recognition of a crop type, an image of the crop fromanother perspective may already be available. For example, when the cropis corn, an image of the corn covered in husk may have been generated,and this GUI can then direct the user to remove the husk to show the earand the kernels and generate another image of the corn. The stageindicator 1608 shows the current stage in the summary or tutorial, suchas the third page of three pages. Selecting the Continue button 1006causes changing program control to the next stage in the summary ortutorial, such as the third page of the three pages.

FIG. 17 illustrates an example computer interaction device showing a GUIgenerated under program control that can display yield information. Inone embodiment, an interaction area 1700 comprises a header panel 1710and a crop image area 1702. In one embodiment, the header panel displaysa title of a function of the associated app which, in the example ofFIG. 17, indicates that the interaction device is then currentlydisplaying data relating to a yield of a crop or a field in which thecrop is located. Selecting a Use button 1716 causes the app to generatea yield report based on the image already taken and currently displayedin the crop image area 1702. Selecting a Retake button 1714 causes theapp to allow retaking of a photo and generation of a corresponding imageto replace the currently displayed image.

In one embodiment, in response to generation of an image and subsequentyield estimation, the computer interaction device can redisplay theimage in the area 1702. The computer interaction device can furtherdisplay the yield estimation data in a yield information area 1720 as anoverlay. In this example, the generated image depicts corn, and thecomputer interaction device displays as yield estimation data includingthe kernel count 1706 of the corn, volume data of the corn 1708including the width, the length, and the total size, and the yieldestimate of a field in which the corn is located 1704 in terms ofbushels per acre. The yield information area 1702 can also include aninformation request button 1712 that allows a user to request more yieldinformation.

What is claimed is:
 1. A computer-implemented method of determining anumber of kernels from an image of a corn, comprising: receiving, by aprocessor, a digital image that is in encoded in color or gray scale;segmenting the image into foreground data associated with a corncomprising a group of kernels grown on an ear of the corn and backgrounddata, to create an updated image; identifying, by the processor,clusters of one or more connected pixels in the foreground data;performing thresholding on a size of each of the clusters, therebyclassifying the foreground data into one or more single-kernel areas andseparate one or more multi-kernel areas based on a size of each of theclusters without further segmenting any of the clusters, each of the oneor more single-kernel areas corresponding to a single kernel, each ofthe one or more multi-kernel areas corresponding to multiple kernels;determining a total number of kernels based on one or more sizes of theone or more single-kernel areas and one or more sizes of the one or moremulti-kernel areas; causing a display of the total number of kernels. 2.The computer-implemented method of claim 1, the segmenting comprising:enhancing the image in contrast to create an enhanced image; dividingthe enhanced image into the foreground data and the background data tocreate a divided image; updating the divided image by removing noisevalues to create an updated image.
 3. The computer-implemented method ofclaim 2, enhancing the image comprising performing contrast limitedadaptive histogram equalization (CLAHE).
 4. The computer-implementedmethod of claim 2, dividing the enhanced image comprising performing aconstant thresholding method and an adaptive thresholding method.
 5. Thecomputer-implemented method of claim 4, the constant thresholding methodbeing Otsu's Thresholding and the adaptive thresholding method beingbased on a weighted sum of neighborhood values with weights forming aGaussian window.
 6. The computer-implemented method of claim 4, dividingthe enhanced image further comprising classifying each pixel asforeground data when the constant thresholding method or the adaptivethresholding method returns an above-threshold determination.
 7. Thecomputer-implemented method of claim 2, updating the divided imagecomprising performing morphological transformations.
 8. Thecomputer-implemented method of claim 1, the performing comprisingclassifying a cluster of the clusters as a single-kernel area when asize of the cluster is not above a certain threshold and classifying thecluster as a multi-kernel area when the size of the cluster is above thecertain threshold.
 9. The computer-implemented method of claim 1, theperforming further comprising identifying an orientation of the corn inthe image and a location within the corn of each of the clusters. 10.The computer-implemented method of claim 1, the determining comprising:computing an aggregate size of the one or more single-kernel areas;dividing a size of each multi-kernel area of the one or moremulti-kernel areas by the aggregate size.
 11. The computer-implementedmethod of claim 1, further comprising estimating a crop yield for agiven crop field based on the total number of kernels and a number ofcrops in the given crop field.
 12. The computer-implemented method ofclaim 11, the estimating being further based on an estimated volume orweight of the corn.
 13. The computer-implemented method of claim 1,further comprising determining a number of bushels per acre based on thetotal number of kernels, a number of crops per acre, and a weight perkernel at standard moisture.
 14. One or more non-transitory storagemedia storing instructions which, when executed by one or more computingdevices, cause performance of a method of determining a number ofkernels from an image of a corn, the method comprising: receiving adigital image that is in encoded in color or gray scale; segmenting theimage into foreground data associated with a corn comprising a group ofkernels grown on an ear of the corn and background data, to create anupdated image; identifying clusters of one or more connected pixels inthe foreground data; performing thresholding on a size of each of theclusters, thereby classifying the foreground data into one or moresingle-kernel areas and separate one or more multi-kernel areas based ona size of each of the clusters without further segmenting any of theclusters, each of the one or more single-kernel areas corresponding to asingle kernel, each of the one or more multi-kernel areas correspondingto multiple kernels; determining a total number of kernels based on oneor more sizes of the one or more single-kernel areas and one or moresizes of the one or more multi-kernel areas; causing a display of thetotal number of kernels.
 15. The one or more non-transitory storagemedia of claim 14, the segmenting comprising: enhancing the image incontrast to create an enhanced image; dividing the enhanced image intothe foreground data and the background data to create a divided image;updating the divided image by removing noise values to create an updatedimage.
 16. The one or more non-transitory storage media of claim 14, theperforming further comprising identifying an orientation of the corn inthe image and a location within the corn of each of the clusters. 17.The one or more non-transitory storage media of claim 14, thedetermining comprising: computing an aggregate size of the one or moresingle-kernel areas; dividing a size of each multi-kernel area of theone or more multi-kernel areas by the aggregate size.
 18. The one ormore non-transitory storage media of claim 14, the method furthercomprising estimating a crop yield for a given crop field based on thetotal number of kernels and a number of crops in the given crop field.19. The one or more non-transitory storage media of claim 18, theestimating being further based on an estimated volume or weight of thecrop.
 20. The one or more non-transitory storage media of claim 14, themethod further comprising determining a number of bushels per acre basedon the total number of kernels, a number of crops per acre, and a weightper kernel at standard moisture.